Ground penetrating radar (GPR) technology has been used for the past 20 years for a variety of applications to assess transportation infrastructure. However, the main issue after all these years remains: "How well does GPR work and under what conditions?" Results show that GPR works well for some situations, but is not an appropriate tool for other situations. It is not used currently on a routine basis by the US Departments of Transportation (DOTs) due mainly to difficulties encountered with data interpretation. Data interpretation difficulties are mainly attributed to the fact that images obtained from the reflected signals are not photographs of the features that are beneath the surface being investigated. The images show the amplitude of the radar-reflected signals from the interfaces with different dielectric properties. Therefore, a considerable amount of experience and operator skill may be required to correctly interpret sub-surface radar results. To better understand reflected GPR signals, this research was conducted with the following objectives: to determine the dielectric properties of concrete over the used GPR frequency range; to synthesize the reflected air-coupled radar signals and compare them with measured waveforms; to model and study the effects of simulated defects in concrete on the reflected air-coupled and ground-coupled radar signals; and to validate the research results in the field by predicting layer thicknesses of flexible pavements and detecting moisture in flexible pavement systems. Several concrete slabs, 1.5x1.5 m, were constructed with known thicknesses, simulated defects, and different reinforcement configurations. The concrete mixes included four different bridge deck mixes and one concrete pavement mix used in the State of Virginia. Results have shown that the dielectric constant of concrete is frequency and mix dependent. However, modeling the reflected signals using an average complex dielectric constant over the entire radar frequency range led to modeled waveforms comparable to the measured waveforms. Although air- and water-filled voids did distort the reflected waveforms, a model was developed to predict the reflected waveforms from the simulated defects. Reinforcement was found to affect the reflected waveforms only when it was oriented in a direction perpendicular to the GPR antennas. A model was also developed to predict the GPR waveforms obtained from flexible pavements. This model could be used in a procedure to measure layer thicknesses more accurately by including losses that occur inside the pavement materials. Two different case studies, where a ground-coupled GPR system was used to locate moisture at different layers, have led to the conclusion that the ground-coupled GPR is a feasible tool to detect moisture inside pavements. / Ph. D.

International Telemetering Conference Proceedings / October 22-25, 2001 / Riviera Hotel and Convention Center, Las Vegas, Nevada / Synthetic-aperture microwave imaging with ground penetrating radar systems has become a research topic of great importance for the potential applications in sensing and profiling of civil and geophysical structures. It allows us to visualize subsurface structures for nondestructive evaluation with microwave tomographic images. This paper provides an overview of the research program, ranging from the formation of the concepts, physical and mathematical modeling, formulation and development of the image reconstruction algorithms, laboratory experiments, and full-scale field tests.

This research project on the campus of Southern Illinois University Carbondale is an extension to the inquiry into the feasibility and reliability of the technology known as Digital Laser Speckle Image Correlation (DiLSIC). This is a hybrid approach of combining two existing technologies. The first being Digital Image Correlation (DIC) which is a nondestructive evaluation commonly used to find displacement, in-plane strain, as well as deformation. The second being the of laser speckle patterns. This hybrid has achieved level of resolution measured to be 3.4μ. DiLSIC increases the application ability of the DIC technique to situations that generally would not be an option to use. DiLSIC needs no artifact speckle patterns to be applied to the specimen as a preparation for nondestructive testing. In DIC testing, the surface of a specimen must artifact speckles applied to the subject surface. Often the application of artifact speckles is not desirable or possible. DiLSIC is an acceptable alternative to the previously discussed industry-wide practice. This method broadens the usage of the DIC technique to situations which previously were not possible. This technology can identify, quantify, and detect the distribution of strain and stress concentrations in composite structures. For this study, a honeycomb-backed glass fiber reinforced polymer (GFRP) panel from a Cessna aircraft exterior luggage door was obtained and a defect panel is created. The panel is constructed with one area containing a repair compliant with manufacturer standardized methods and a repair area is not compliant and consists of multiple incorrect repair steps. An area with no repair is also tested to act as a control for comparison and quantification. The results for the inspected areas showed a linear strain increase in the noncompliant repair. The data plot for the compliant repair showed a trend of following the same basic curve as the no repair area. A verification process follows the DiLSIC testing consisting of using Infrared Thermography, Air-coupled ultrasonic, and white light artifact speckle DIC. These tests show DiLSIC is a viable alternative to the testing that is available in the industry. DiLSIC can detect defect location, size, geometry and map strain to determine the difference between compliant and noncompliant repairs when compared to a base level non-repair area

Characterizing hidden solder joint shapes is essential for electronics reliability. Active thermography is a methodology to identify hidden defects inside an object by means of surface abnormal thermal response after applying a heat flux. This research focused on understanding, modeling, and predicting hidden solder joint shapes. An experimental model based on active thermography was used to understand how the solder joint shapes affect the surface thermal response (grand average cooling rate or GACR) of electronic multi cover PCB assemblies. Next, a numerical model simulated the active thermography technique, investigated technique limitations and extended technique applicability to characterize hidden solder joint shapes. Finally, a prediction model determined the optimum active thermography conditions to achieve an adequate hidden solder joint shape characterization. The experimental model determined that solder joint shape plays a higher role for visible than for hidden solder joints in the GACR; however, a MANOVA analysis proved that hidden solder joint shapes are significantly different when describe by the GACR. An artificial neural networks classifier proved that the distances between experimental solder joint shapes GACR must be larger than 0.12 to achieve 85% of accuracy classifying. The numerical model achieved minimum agreements of 95.27% and 86.64%, with the experimental temperatures and GACRs at the center of the PCB assembly top cover, respectively. The parametric analysis proved that solder joint shape discriminability is directly proportional to heat flux, but inversely proportional to covers number and heating time. In addition, the parametric analysis determined that active thermography is limited to five covers to discriminate among hidden solder joint shapes. A prediction model was developed based on the parametric numerical data to determine the appropriate amount of energy to discriminate among solder joint shapes for up to five covers. The degree of agreement between the prediction model and the experimental model was determined to be within a 90.6% for one and two covers. The prediction model is limited to only three solder joints, but these research principles can be applied to generate more realistic prediction models for large scale electronic assemblies like ball grid array assemblies having as much as 600 solder joints.

Quick mass production of homogeneous thin film material is required in paper, plastic, fabric, and thin film industries. Due to the high feed rates and small thicknesses, machine vision and other nondestructive evaluation techniques are used to ensure consistent, defect-free material by continuously assessing post-production quality. One of the fastest growing inspection areas is for 0.5-500 micrometer thick thin films, which are used for semiconductor wafers, amorphous photovoltaics, optical films, plastics, and organic and inorganic membranes. As a demonstration application, a prototype roll-feed imaging system has been designed to inspect high-temperature polymer electrolyte membrane (PEM), used for fuel cells, after being die cast onto a moving transparent substrate. The inspection system continuously detects thin film defects and classifies them with a neural network into categories of holes, bubbles, thinning, and gels, with a 1.2% false alarm rate, 7.1% escape rate, and classification accuracy of 96.1%. In slot die casting processes, defect types are indicative of a misbalance in the mass flow rate and web speed; so, based on the classified defects, the inspection system informs the operator of corrective adjustments to these manufacturing parameters. Thickness uniformity is also critical to membrane functionality, so a real-time, full-field transmission densitometer has been created to measure the bi-directional thickness profile of the semi-transparent PEM between 25-400 micrometers. The local thickness of the 75 mm x 100 mm imaged area is determined by converting the optical density of the sample to thickness with the Beer-Lambert law. The PEM extinction coefficient is determined to be 1.4 D/mm and the average thickness error is found to be 4.7%. Finally, the defect inspection and thickness profilometry systems are compiled into a specially-designed graphical user interface for intuitive real-time operation and visualization.

Concrete is the most widely used construction material in the world, so the assessment of damage in concrete is critical from the point of view of both safety and cost. Of particular interest are macro cracks that extend through the concrete cover of the reinforcement, which can potentially expose the reinforcement to corrosive elements. The high density of scatterers such as aggregate and voids in concrete makes quantitative imaging with coherent ultrasound difficult. As an alternative, this research focuses on diffuse energy based ultrasonic methods rather than coherent ultrasonic methods for crack depth assessment. Two types of ultrasonic measurements were made on real cracks formed under four point bending: one that focuses on time of flight measurements from an impactor; while the other uses the arrival time of maximum energy in a diffuse field excited by an impulsive load from a transducer. Each of these ultrasonic techniques is used to interrogate a macro crack in a concrete beam, and the results are compared to determine their accuracy and robustness. The actual crack depth is determined using direct surface measurements and a destructive dye-injected approach with drilled cores. The results suggest that the diffusion method, using a maximum energy approach, more accurately estimates the crack than visual inspection and impact echo methods, which overestimate the depth.

Quick mass production of homogeneous thin film material is required in paper, plastic, fabric, and thin film industries. Due to the high feed rates and small thicknesses, machine vision and other nondestructive evaluation techniques are used to ensure consistent, defect-free material by continuously assessing post-production quality. One of the fastest growing inspection areas is for 0.5-500 micrometer thick thin films, which are used for semiconductor wafers, amorphous photovoltaics, optical films, plastics, and organic and inorganic membranes. As a demonstration application, a prototype roll-feed imaging system has been designed to inspect high-temperature polymer electrolyte membrane (PEM), used for fuel cells, after being die cast onto a moving transparent substrate. The inspection system continuously detects thin film defects and classifies them with a neural network into categories of holes, bubbles, thinning, and gels, with a 1.2% false alarm rate, 7.1% escape rate, and classification accuracy of 96.1%. In slot die casting processes, defect types are indicative of a misbalance in the mass flow rate and web speed; so, based on the classified defects, the inspection system informs the operator of corrective adjustments to these manufacturing parameters. Thickness uniformity is also critical to membrane functionality, so a real-time, full-field transmission densitometer has been created to measure the bi-directional thickness profile of the semi-transparent PEM between 25-400 micrometers. The local thickness of the 75 mm x 100 mm imaged area is determined by converting the optical density of the sample to thickness with the Beer-Lambert law. The PEM extinction coefficient is determined to be 1.4 D/mm and the average thickness error is found to be 4.7%. Finally, the defect inspection and thickness profilometry systems are compiled into a specially-designed graphical user interface for intuitive real-time operation and visualization.

[pt] Esta tese associa duas técnicas de fronteira na área de Ensaios Não-Destrutivos magnéticos, que são a utilização do magnetômetro supercondutor SQUID como instrumento de medida e de Redes Neurais como ferramentas de análise dos sinais detectados. Medidas pioneiras com o SQUID foram realizadas em amostras de aço e de alumínio contendo defeitos diversos, e foram idealizados e implementados dois Sistemas Neurais, os quais utilizaram combinações de vários tipos de redes neurais para, a partir do campo magnético medido, obter informações a respeito da geometria dos defeitos, possibilitando assim estimar sua gravidade. / [en] This thesis combines two state-of-the-art techniques in the area if magnetic Nondestructive Evaluation, that is, the application of the superconducting magnetometer SQUID as the magnetic sensor, and the use of Neural Networks as analysis tools for the detected magnetic signals. Pioneering measurements using the SQUID have been made in steel and aluminum samples with various types of flaws, and two Neural Systems have been implemented, based on the combination of several neural networks algorithms. Such systems aim to, based on the measured magnetic field, obtain information about defect geometry, thus allowing the assessment of defect severity.

[pt] Desenvolveu-se um método para Ensaios Não-Destrutivos de placas condutoras, utilizando o dispositivo supercondutor conhecido como SQUID (Superconducting Quantum Interference Device), disponível no Laboratório de Supercondutividade Aplicada e Magnetismo do Departamento de Física da PUC- Rio. O trabalho pode ser dividido em duas áreas: método experimental para obtenção das medidas magnéticas e algoritmos de processamento de imagens para realçar estes dados. / [en] It was developed a Nondestructive Evaluation method for conducting plates, using the superconducting device known as SQUID (Superconducting Quantum Interference Device), which is available in the Laboratory of Applied superconductivity and Magnetism of the Departament of Physics of PUC-Rio. The work may be separated in two fields: experimental method for obtaining the magnetic measures and image processing algorithms used to enhance this data.

Digital Laser Speckle Image Correlation (DiLSIC) is a technique that utilizes a laser generated speckle pattern with Digital Image Correlation (DIC). This technology eliminates the need to apply an artifact speckle pattern to the surface of the material of interest, and produces a finer speckle pattern resulting in a more sensitive analysis. This investigation explores the parameters effecting laser speckle patterns for DIC and studies DiLSIC as a tool to measure surface strain and detect subsurface defects on pressure vessels. In this study a 632.8 nm 30 mW neon-helium laser generated the speckle pattern by passing through the objective end of an objective lens. All experiments took place in a lab setting on a high performance laminar flow stabilizer optical table.This investigation began with a deeper look at the camera settings that effect the effectiveness of using laser speckles with DIC. The first studies were concentrated on the aperture size (f-stop), shutter speed, and gain (ISO) of the camera. Through a series of zero-correlation studies, translation tests, and settings studies, it was discovered that, much like white light DIC, an increased gain allowed for more noise and less reliable measurements when using DiLSIC. It was shown that the aperture size and shutter speed will largely depend on the surface composition of the material, and that these factors should be investigated with each new sample of different surface finish.To determine the feasibility of using DiLSIC on pressure vessels two samples were acquired. The first was a standard ASTM filament wound composite pressure vessel (CPV) which had an upper load limit of 40 psi. The second was a plastic vessel that had internal subsurface defects added with the use of an air pencil grinder. Both vessels were put under a pressure load with the use of a modified air compressor that allowed for multiple loading cycles through the use of a pressure relief valve. The CPV was mapped out in 10-degree increments between the 90° and 180° markings that were on the pressure vessel, occurring in three areas, each one inch apart. The CPV had a pressure load applied to at 10, 20, 30,and 40 psi. DiLSIC was able to measure increasing displacement with increased loading on the surface of the CPV, however with a load limit of 40 psi no strains were detected. The plastic vessel had known subsurface defects, and these areas were the focus of the investigation. The plastic vessel was loaded with a pressure load at 5, 10, 12, 15, 17, and 20 psi. The 5 psi loaded image was used as a reference image for the correlation and decorrelation consistently occurred at 20 psi. This investigation proved that DiLSIC can detect and locate subsurface defects through strain measurement. The results were verified with traditional white light DIC, which also showed that the subsurface defects on pressure vessels were detectable. The DIC and DiLSIC results did not agree on maximum strain measurement, with the DiLSIC prediciting much larger strains than traditional DIC. This is due to the larger effect out-of-plane displacement has on DiLSIC. DiLSIC was able to detect subsurface defects on a pressure vessel. The median measured hoop strain was in agreement for DiLSIC, DIC and the predicted hoop strain for a wall thickness of 0.1 inches. However, DiLSIC also produced unreliable maximum strain measurements. This technique shows potential for future applications, but more investigations will be needed to implement it for industrial use. A full investigation into the parameters surrounding this technique, and the factors that contribute the most to added noise and unreliability should be conducted. This technology is being developed by multiple entities and shows promising results, and once further advanced could be a useful tool for rapid surface strain measurement and subsurface defect detection in nondestructive evaluation applications. Therefore, it is recommended to continue further investigations into this technology and its applications.